Course details

Applied Deep Learning: Unsupervised Data

Applied Deep Learning: Unsupervised Data


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 11-video course explores the concept of deep learning and implementation of deep learning-based frameworks for natural language processing (NLP) and audio data analysis. Discover the architectures of recurrent neural network (RNN) that can be used in modeling NLP, and the challenges of unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature machine learning. First, examine the prominent statistical classification models and compare generative classifiers with discriminative classifiers; then recall different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model. Learn about setting up and working with PixelCNN; explore differences between multilayer perception (MLP), convolutional neural network (CNN), and RNN. Explore the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. Finally, take a look at encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. The concluding exercise involves recalling RNN architecture that can be used in modeling NLP, variants of ResNet, and setting up PixelCNN.



Expected Duration (hours)
1.5

Lesson Objectives

Applied Deep Learning: Unsupervised Data

  • discover the key concepts covered in this course
  • recall the concept of deep learning and the approach of using deep learning-based frameworks to model NLP tasks and audio data analysis
  • describe the role of recurrent neural network and the various architectures of recurrent neural network that can be used in modeling natural language processing
  • recognize the challenges associated with unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature learning
  • describe the prominent statistical classification models and compare generative classifiers with discriminative classifiers
  • recall the different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model
  • demonstrate the steps involved in setting up and working with PixelCNN
  • describe the characteristics of the different classes of artificial neural networks and the difference between MLP, CNN, and RNN
  • recognize the essential capabilities and variants of ResNet that can be used for computer vision and deep learning
  • describe encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders
  • recall the prominent architectures of recurrent neural network that can be used in modeling natural language processing, list the variants of ResNet that can be used for computer vision and deep learning, and set up PixelCNN
  • Course Number:
    it_mlacnndj_01_enus

    Expertise Level
    Intermediate